ABSTRACT Obstructive sleep apnea (OSA) is highly prevalent in the United States and its prevalence is increasing with the obesity epidemic1. OSA is independently associated with increased sudden cardiac death (SCD) risk2. The majority of SCD events in middle-aged adults occur without prior history of cardiovascular disease and are due to cardiac arrhythmias. There is great need for improved understanding of the pathophysiology and risk stratification of arrhythmogenic risk in individuals with OSA. Because most of these events occur during sleep, tools that can combine high-fidelity electrocardiography (ECG) data with simultaneous polysomnography information are needed to study the OSA phenotype that is at risk and to test their response to therapy. This application proposes the development of a user-friendly interface to synchronize high-fidelity 12- lead ECG recordings with polysomnography information. This software interface will permit comprehensive assessment of markers of ventricular arrhythmia susceptibility (ventricular ectopy burden, heart rate turbulence [HRT] and microvolt T-wave alternans [mTWA]) synchronous with heart rate variability (HRV) in the time and frequency domains, respiratory events, degree of hypoxia, sleep staging and cerebral cortical activity in individuals with OSA. We will use previously validated algorithms for calculating HRV, ventricular ectopy burden, HRT, and precordial mTWA. This analysis tool will be tested using pre-existing research data sets collected during the PIs current NHLBI K23 award (HL094760) and using data from newly recruited subjects before positive airway pressure therapy and during its titration. The study will collect pilot data for a planned R01 proposal to characterize the OSA phenotype that is at higher risk for sudden cardiac death and to study its response to PAP therapy. Our ECG-polysomnogram analysis tool will be designed to work across different vendor platforms and will provide synchronous physiologic assessment of the effects of OSA in cardiac arrhythmia risk. Understanding these associations will ultimately help tailor and monitor OSA treatment, but also be used to study sleep physiology and arrhythmogenic risk in other patient populations.